‘ptspotter’ is a package that aims to simplify some of the mundane tasks involved in setting up an analytical pipeline using the fantastic ProjectTemplate framework.
ptspotter is available on CRAN.
This data pipeline was redeveloped in December 2020 as Brexit approached. It has been an important source of road sensor data to help inform the movement of traffic around English ports. This redevelopment was requested by the faster economic indicators team.
The redeveloped pipeline features the querying of the Highways England RESTful webTRIS api in parallel using a virtual environment. Additional value beyond the MVP was added with a Shiny UI to add data validation and an automated Rmarkdown report adding insight on the proportion of site types returning null responses.
These applications are presented in reverse order of publication. While the contexts of the applications vary, they help to illustrate an increasing maturity in Shiny development.
This is an educational application designed to help trainee data scientists in developing their understanding of the different data join functions available within the {dplyr} package. Unifyr allows the learner to select subsets from the well-known gapminder dataset and observe the output of specified join functions.
Click the image to see Unifyr on shinyapps.io, opens in new window.
This Google Mobility Data application was developed at the start of the Covid-19 pandemic using data derived by the optical processing of pdf data publications, an innovative approach to improving the accessibility of Google’s publications.
The application allows the user to select specified local authority or NHS bodies in order to view the time series mobility data.
Click the image to see Google mobility data on shinyapps.io, opens in new window.
This application combines 2 open datasets published by Welsh Government: Budgeted educational revenue and outturn expenditure. The available data dimensions may be selected from to plot upon the chart axes by educational phase. Time series data for all available schools and data dimensions may also be viewed upon the second tab.
Click the image to see Welsh school funding data on shinyapps.io, opens in new window.
This app was a bit of fun and took very little time. It used some basic language processing and geolocation to plot Welsh location names with common prefixes such as “Cwm” or “Aber” on a map. The application used open data published by the Welsh Language Commissioner who subsequently approached me to share the application codebase with them.
Click the image to see common Welsh place names on shinyapps.io, opens in new window.
This repo visualises LiDar altitude data of Cardiff openly published by Natural Resources Wales. Cardiff was selected due to comparatively high data quality and population density. The visualisations compare current day high tide altitude with a modelled 2 metre sea rise by the year 2100.
Click the image to see common Cardiff high tide levels on shinyapps.io, opens in new window.
In 2018 I had been approached by a colleague to discuss the feasibility of using satellite data to help estimate the quantity of woodland stock in a defined rural area. The report documents my exploration work done in this area using open source geospatial frameworks and satellite rasters. The work pointed to a promising avenue of analysis but also indicated limitations in the use of open source landsat imagery. At the time I recommended the procurement of higher resolution remote imagery and verification of tree counts using ground truth observational work.
Click the image to see the counting trees from space exploratory report on RPubs, opens in new window.
This {flexdashboard} user interface was one of my first pieces of work in application development. The dashboard is an adapted RMarkdown output and can very easily be reproduced with some basic data manipulation and markdown syntax. Furthermore, the product is self-contained and can be Emailed and freely shared, unlike a Shiny application which requires a connection to R and a shiny server.
The dashboard presents Budgeted Education Revenue for financial years 2018/19. An interactive map of geolocated schools in Wales is presented with informative tooltips highlighting the data dimensions. In additional tabs, linear regression models of pupil numbers against delegated budgets for each educational phase are presented.
Click the image to see the Welsh school funding dashboard on RPubs, opens in new window.